\subsection{Method I}
\label{sec:method1}
First, we investigate a naive algorithm for predicting graphs. 
Given an initial input graph $G$, we first compute its projected database $\mathcal{D}_G$. Then, for each graph $E, E \in \mathcal{D}_G, E \in B(G,1)$, we calculate the edit operation from $G$ to $E$.   
Finally, the edit operation whose resulting graph having the highest support is determined. This algorithm is naive in the sense that it considers the whole graph at once. This is akin to an hidden Markov Model formulation where the state of the model is the graph itself and actions are edit operations. 

The algorithm performs well for small graph sizes. This is encouraging, however we would expect that the naive method would fail for larger graphs. By taking into account the overall structure of $G_p$ (defined in section~\ref{sec:preliminaries} as a whole, the algorithm misses to capture the functional patterns with which humans have designed indoor floorplans. 
As an example, when a rare vertex is connected to a frequently occuring part of the input graph, the algorithm only considers those graphs which include the rare vertex disregarding others, ignoring the functional aspect of subparts of an indoor topology.

%As an example, the partial graph $\{ corridor-elevator-female \: lavatory\}$ is often completed with $stair$ since functionally  a $stair$ act as a backup mechanism in case elevators fail in a building. Therefore they are connected to each other and frequently occur together in the dataset. However, the naive algorithm may predict $office$ instead because it is the most frequenly connected node to $corridor$. 


